CS595D
Process Discovery and Model Enhancement
Winter 2015

Applying and extending techniques of data mining to re-discover business process models has becoming popular in recent years. This can help not only finding process models in cases where there were typically no rigorous design for business process (e.g., case management applications), but also finding interesting unknown regularities of process models. The result of process mining can provide very useful information for improving process models and process management. Business process execution typically follow some pre-designed process models. However, process models are usually following by a majority but not all process executions since business processes often encounter exceptional situations where detour to the existing process models are necessary. Periodically, the enterprise needs to revise process models based on the past logged executions in order to guide a majority of future workflow executions. This problem of revising process models using the execution log is call process model enhancement. Clearly, process model enhancement can benefit from techniques from process mining and process analysis. In this seminar, we plan to learn the basic problems and techniques of process discovery and model enhancement developed in the literature.

• Regular meeting times: Mondays, 10:30am-12noon
• Location: HFH Room 1152
• Instructor: Jianwen Su
• Units: 2
• Enroll code: 77107

• Monday, Mar 9: No meeting

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• Tuesday, Jan 20, 3:00pm-4:00pm
  Repairing Process Models to Reflect Reality.
  Dirk Fahland and Wil M. P. van der Aalst. Proc. Int Conf. on Business Process Management (BPM), 2012
• Monday, Jan 26
  Exception Handling for Repair in Service-Based Processes
  Gerhard Friedrich, Mariagrazia Fugini, Enrico Mussi, Barbara Pernici, and Gaston Tagni. IEEE Trans. on Software Eng., 36(2):198-215
• Monday, Feb 2
  Conformance Checking Using Cost-Based Fitness Analysis.
  A. Adriansyah, B.F. van Dongen, and W.M.P. van der Aalst. Proc. 15th IEEE Int. Conf. on Enterprise Distributed Object Computing (EDOC), 2011
• Monday, Feb 9
  Process Mining and Security: Detecting Anomalous Process Executions and Checking Process Conformance.
  W.M.P. van der Aalst and A.K.A. de Medeiros. Electronic Notes in Theoretical Computer Science, 121:3–21, 2005
• Monday, Feb 23
  Diagnosing and Repairing Data Anomalies in Process Models.
  Ahmed Awad, Gero Decker, and Niels Lohmann. BPM 2009 Int Workshops: Revised Papers, Springer, 2010, pp.5-16
• Monday, Mar 2
  Paper to discuss: Time prediction based on process mining.
  W.M.P. van der Aalst, M.H. Schonenberg, and M. Song. Information Systems, 36(2):450–475, April 2011

1. Remco Dijkman, Marlon Dumas, Boudewijn van Dongen, Reina Kaarik, and Jan Mendling.
   Similarity of business process models: Metrics and evaluation. Information Systems, 36(2):498–516, April 2011
2. Helen Schonenberg, Barbara Weber, Boudewijn van Dongen, and Wil van der Aalst.
   Supporting Flexible Processes through Recommendations Based on History. Prof. Int. Conf. on Business Process Management (BPM), 2008, pp.51-66
3. Anastasiia Pika, Moe T. Wynn, Colin J. Fidge, Arthur H. M. ter Hofstede, Michael Leyer, and Wil M. P. van der Aalst.
   An Extensible Framework for Analysing Resource Behaviour Using Event Logs. Proc. Int. Conf. Advanced Information Systems (CAiSE), 2014, pp. 564-579
4. Wil M. P. van der Aalst, Hajo A. Reijers, and Minseok Song.
   Discovering Social Networks from Event Logs. Computer Supported Cooperative Work (CSCW), 14(6):549-593, December 2005
5. Fabrizio Maria Maggi, Domenico Corapi, Alessandra Russo, Emil Lupu, and Giuseppe Visaggio.
   Revising Process Models through Inductive Learning. In BPM 2010 Int. Workshops and Education Track: Revised Selected Papers, Springer, 2011, pp.182-193

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